Search Results (175)

Increasing consumption of plant-based alternatives is promoted to reduce the environmental impact of food systems, yet adoption remains limited. The aim of this study was to identify distinct consumer segments and examine differences in their perceptions, consumption habits, and trial intentions concerning plant-based dairy alternatives (PBDAs). Conceptually, it advances PBDAs segmentation by jointly incorporating pro-dairy justifications, avoidance of animal-origin considerations, and self-reported PBDAs familiarity, capturing psychological defence mechanisms alongside knowledge-related influences on adoption. Data were collected in a nationwide cross-sectional CAWI survey of 1220 Polish adults responsible for household food purchasing, stratified and quota-matched by gender, age, region, and settlement size. Factor analysis of the segmenting variables was conducted using principal component analysis with varimax rotation, followed by two-step cluster analysis. Alternative cluster solutions were compared using the Bayesian Information Criterion based on the log-likelihood (BIC-LL). The selected five-cluster solution showed acceptable to good clustering quality, as indicated by silhouette-based measures of cohesion and separation. Given the cross-sectional CAWI design and reliance on self-reported measures, the findings do not allow causal inference and should be interpreted as context-specific to the Polish, dairy-centric food culture. Cluster analysis identified five segments that differed in PBDA-related beliefs, product image evaluations, consumption patterns, and trial intentions. PBDA-oriented segments, comprising a dairy-critical segment and a dual-consumption segment, exhibited higher perceived familiarity and stronger ethical and environmental concerns and showed greater PBDA use and willingness to try new products. The dual-consumption segment reported the highest use and trial readiness. In contrast, resistant segments showed stronger dairy attachment, lower perceived familiarity, and more sceptical evaluations of PBDAs’ healthfulness, naturalness, and sensory appeal, and rarely consumed plant-based alternatives. The findings highlight substantial heterogeneity in how Polish dairy consumers perceive PBDAs, emphasising the importance of segment-specific approaches for communication and product development. Tailored strategies can help address the diverse motivations and barriers of consumers, supporting a dietary shift toward more plant-based options. Full article

Effective risk communication designed for risk management increases concern and motivates action by providing guidance and specific actions that can be taken. When exposures to environmental contaminants or stressors are ubiquitous or pollutant sources are not easily controlled, also decreasing sustainability, risk communication is focused on actions for risk reduction and avoidance. Three recommended practices (use of virtual exemplars, narrative, and social media) are discussed as tactics and platforms to inform public beliefs and behaviors and to encourage adoption of long-term planning goals that avoid the consequences of future risks. These risk communication strategies appeal broadly to lay audiences, are not limited to scientists and science-trained risk communicators, and are consistent with the US EPA’s SALT Framework, a research-based approach with recommended practices to guide risk communication. The overall strategy is to make risk communication more effective by using approaches that are dynamic, interactive, engaging, and relatable. Full article

Key agreement protocols based on neural synchronization with Tree Parity Machines (TPMs) offer promising security advantages: they do not rely on trapdoor functions, making them resistant to quantum attacks, and they avoid the need for specialized hardware required by quantum-based schemes. Nevertheless, these protocols face a significant vulnerability: the large number of public message exchanges required for synchronization increases the risk that an attacker, acting as a Man-in-the-Middle, can successfully synchronize their own TPMs with those of the legitimate parties and ultimately recover the shared key. Motivated by the need to reduce this risk, we propose a novel probabilistic protocol that enables two parties to securely estimate the size of the shared key during intermediate steps, without revealing any key material. This estimation allows the protocol to terminate as soon as sufficient key material has been established, thereby reducing the number of synchronization rounds and limiting the opportunity for an attacker to synchronize. We integrate our estimation mechanism into a neural key agreement protocol and evaluate its performance and security, demonstrating improved efficiency and enhanced resistance to attacks compared to existing approaches. Full article

Despite much interest in the flow experienced by English as a Foreign Language (EFL) teachers, there is less research on flow re-engagement and pre-service teachers at the crucial phase of career development. This study aims to examine flow dynamics among pre-service and in-service EFL teachers in China during teaching. Six Chinese EFL teachers (three pre-service and three in-service) engaged in two rounds of interviews over the course of one year, which were analyzed using a thematic narrative approach. The findings indicate that immediate feedback, clear goals, and a challenge-skill balance were key antecedents of flow. In-service teachers highlighted principal’s teaching-focused philosophy, technology support, teaching experience and curiosity. All participants reported a sense of control, deep absorption, and time distortion. Two experienced teachers further claimed a loss of self-consciousness. The flow of participants was impeded by student-related factors, strong self-consciousness, and technological breakdowns. In-service teachers noted more complicated causes. To re-enter a state of flow, pre-service teachers favored avoidance strategies, whereas in-service teachers employed more flexible approaches. Flow enhanced instructors’ teaching confidence, shifted pre-service teachers’ career motivation and fostered in-service educators’ professional well-being, post-class reflection, and self-improvement. Administrators and teacher educators should provide a teaching-oriented working environment for in-service teachers and offer flow-focused training to pre-service teachers, thus promoting their flow experiences and fostering sustainable professional development. Full article

Motivated by the strong seasonality of East Asian meteorology and its control on pollution episodes characterized by fluctuation level, we model the season-resolved climatology of the regional PM_2.5 connectivity over the Korean Peninsula. Using daily AirKorea data for 2016–2020, we (i) remove daily climatology and the peninsula-wide background (empirical orthogonal function; EOF1) to obtain residual signals; (ii) compute the sign-preserving multifractal detrended cross-correlation coefficient MFDCCA-$\rho \left(q,s\right)$; (iii) apply iAAFT surrogate significance across scales; and (iv) construct signed, weighted networks aggregated over short (5–15 d) and mid (15–30 d) bands for DJF/MAM/JJA/SON. Our analysis targets the seasonal climatology of fluctuation-level ($q$-dependent) connectivity by pooling seasons across years; this approach increases statistical robustness at 5–30-day scales and avoids diluting season-specific organization. We find negligible connectivity for $q<0$ (small fluctuations) but dense, seasonally organized networks for $q>0$ (strongest in winter–spring and at 15–30 days). After removing the EOF1, positive subgraphs form assortative regional backbones, while negative subgraphs reveal a northwest–southeast anti-phase dipole; the connectivity around Baengnyeongdo (B) highlights a transboundary sentinel role in cool seasons. These results demonstrate that a season-resolved, fluctuation-level framework effectively isolates regional connectivity that would otherwise be masked in annual aggregates or by the peninsula-wide background. Full article

In multi-manipulator systems operating within shared workspaces, achieving collision-free posture control is challenging due to high degrees of freedom and complex inter-manipulator interactions. Traditional motion planning methods often struggle with scalability and computational efficiency in such settings, motivating the need for learning-based approaches. This paper presents a multi-agent deep reinforcement learning (MADRL) framework for real-time collision-free posture control of multiple manipulators. The proposed method employs a line-segment representation of manipulator links to enable efficient interlink distance computation to guide cooperative collision avoidance. Employing a centralized training and decentralized execution (CTDE) framework, the approach leverages global state information during training, while enabling each manipulator to rely on local observations for real-time collision-free trajectory planning. By integrating efficient state representation with a scalable training paradigm, the proposed framework provides a principled foundation for addressing coordination challenges in dense industrial workspaces. The approach is implemented and validated in NVIDIA Isaac Sim across various overlapping workspace scenarios. Compared to conventional state representations, the proposed method achieves faster learning convergence and superior computational efficiency. In pick-and-place tasks, collaborative multi-manipulator control reduces task completion time by over 50% compared to single-manipulator operation, while maintaining high success rates (>83%) under dense workspace conditions. These results confirm the effectiveness and scalability of the proposed framework for real-time, collision-free multi-manipulator control. Full article

This study explores how sustainability leadership and Green Human Resource Management (Green HRM) practices interplay to cultivate an environmentally responsible culture in organizations based in Jeddah. Through thematic analysis of participant interviews, the research identifies key leadership behaviors, such as visionary communication, role modeling, and operational integration, that align culturally grounded ethical values to drive sustainability. Green HRM practices, including green recruitment, targeted training, eco-friendly performance appraisals, and recognition systems, further reinforce these leadership efforts. The study highlights the importance of authentic alignment between leadership values and HRM policies to avoid perceptions of greenwashing and to institutionalize sustainable practices effectively. Findings emphasize that embedding sustainability within organizational culture requires a synergistic approach integrating leadership vision, HRM systems, and cultural context, fostering employee motivation and long-term environmental commitment. The implications provide valuable insights for organizations seeking to implement meaningful sustainability strategies aligned with both global goals and local values. Full article

Background/Objectives: Approach-avoidance behavior is critical for adaptive behavior. The neural basis of these behaviors has been investigated extensively, but the effect of odor valence is unclear. This study tested how positive, negative, and neutral odors affect behavior and event-related potentials (ERPs) in the approach-avoidance task (AAT). Methods: Thirty-two healthy participants performed an AAT. We measured reaction time, accuracy, and ERP components (P1, N1, N2, P3) to understand the process of motivational processing over time. Results: Participants responded faster and more accurately when the direction and target type were congruent under all odor conditions. Odors did not change this core consistent pattern. In contrast, ERP results revealed stage-specific modulations. P1 and N1 components reflected odor-related changes in early sensory processing. The N2 effect present under the air condition was largely absent under positive and negative odors. This suggests reduced conflict monitoring. P3 amplitudes were consistently larger for avoidance than for approach responses, regardless of odor valence. Conclusions: The results demonstrate that odor valence reorganized the neural dynamics of the AAT without changing behavioral performance. This finding shows that olfactory valence modulates attention and control mechanisms and plays a unique role in regulating human motivation. Full article

Accurate and scalable evaluation in team sports remains challenging, motivating the use of artificial intelligence models to support objective athlete assessment. This study develops and validates a predictive model capable of calibrated, operationally tested classification of team-sport athletes as high- or low-performance using a synthetic, literature-informed dataset (n = 400). Labels were defined a priori by simulated group membership, while a composite score was retained for post hoc checks to avoid circularity. LightGBM served as the primary classifier and was contrasted with Logistic Regression (L2), Random Forest, and XGBoost (v3.0.5). Performance was evaluated with stratified, nested 5 × 5 cross-validation. Calibrated, deployment-ready probabilities were obtained by selecting a monotonic mapping (Platt or isotonic) in the inner CV, with two pre-specified operating points: screening (recall-oriented; precision ≥ 0.70) and shortlisting (F1-optimized). Under this protocol, the model achieved 89.5% accuracy and ROC-AUC 0.93. SHAP analyses indicated VO₂max, decision latency, maximal strength, and reaction time as leading contributors with domain-consistent directions. These results represent a proof-of-concept and an upper bound on synthetic data and require external validation. Taken together, the pipeline offers a transparent, reproducible, and ethically neutral template for athlete selection and targeted training in team sports; calibration and pre-specified thresholds align the approach with real-world decision-making. Full article

Approach or avoidance behaviors toward appetitive stimuli, such as alcohol and food, reflect the engagement of motivational states that are fundamental to adaptation of human behavior. Investigating early motor or neural responses to these stimuli provides valuable insights into the underlying mechanisms of these behaviors. This study employed an integrative approach combining postural and electrophysiological measures to explore the impact of alcohol consumption levels on early postural and neural responses to visual alcohol and food stimuli. The objective was to identify early automatic markers of approach or avoidance, and to examine correlations between motor and neural responses. Forty-six participants were divided into two groups (“Low” and “High”) according to their level of alcohol consumption (AUDIT scores). They were exposed to images of alcoholic beverages, non-alcoholic beverages, and appetitive or neutral foods. Postural responses were recorded using a force platform, and brain activity was measured via EEG. Displacement of the center of pressure along the anteroposterior axis, as well as the P100 and N100 components, were analyzed. “High” participants exhibited greater anterior postural displacement in response to alcohol during the first two seconds of stimulus exposure. In contrast, “Low” participants showed early avoidance responses. Significant correlations were found between event-related potential (ERP) wave latencies and postural displacement during the first second of exposure to alcohol-related stimuli. AUDIT scores were also positively correlated with early postural displacement and N100 latency following the viewing of alcoholic beverage images. Early perceptual and motor responses are modulated by alcohol consumption habits. These findings support the value of integrative EEG–posture approaches for identifying implicit motivational markers. Full article

Additive manufacturing (AM) developments have been strongly driven by the ability of AM to improve the strength-to-weight ratios of structures, in contrast to traditional manufacturing methods, heavily supported by lattice structures. These motivations have persisted with the development of large-scale additively manufactured structures, which can offer more flexibility in manufacturing location and can often be faster than traditional manufacturing. However, current large-scale AM methods are often limited by their precision in order to maintain speed, constraining the method to manufacturing simple structures and often avoiding lattices altogether. This work proposes a mathematical framework for defining an equation-based lattice that splits the lattice into (1) build direction and (2) planar components such that their design can be altered to address AM methods restricted to three degrees of freedom. The framework is applied against a class of lattices called triply periodic minimal surfaces, which are represented using implicit equations, and it is shown that this approach allows for their use in large-scale AM technologies and enables further design control for small-scale AM design. Full article

(1) Background: Help-seeking behavior is a key way to maintain health and seek effective treatment, and it also helps to improve patients’ self-management ability. This study aimed to investigate the facilitating and hindering factors of help-seeking behaviors among patients with chronic diseases concerning their health issues. (2) Methods: Based on the Capability, Opportunity, and Motivation-Behavior (COM-B) model, 18 patients with chronic diseases in a tertiary hospital in Zhengzhou City, Henan Province, were selected for semi-structured in-depth interviews between July and November 2024 using a descriptive qualitative research approach. The collected data were analyzed using directed content analysis. (3) Results: A total of 18 interviews were conducted, and two themes and six sub-themes were extracted. The factors that promote health help-seeking behavior in patients with chronic diseases include ability (self-health monitoring ability, sufficient communication preparation ability), opportunity (health support in social bonds, effective support of medical staff), and motivation (good illness identity, past successful experience of health seeking help). Barriers include ability (symptom attribution bias, difficulty in identifying health information), opportunity (heavier financial burden, poor sense of gain in interactions), and motivation (fear and avoidance, stigma of illness). (4) Conclusions: There are some hindering factors and obvious contributing factors regarding health help-seeking behavior among patients with chronic diseases. Medical staff should prioritize guiding patients to seek help for health problems. The COM-B model can be applied to develop targeted intervention strategies for improving help-seeking behavior. This approach is beneficial for enhancing patients’ health management capabilities by promoting proactive health help-seeking practices. Full article

In spite of the ever-growing body of research in achievement goal profiles and their contribution to performance, the research on young children is quite limited. This study examined achievement goal profiles related to mathematics and literacy performance among third-grade students (N = 185, M = 8.73 years; 98 girls), using Latent Profile Analysis. Four distinct profiles emerged—Mastery-Oriented, Approach-Oriented, High Multiple-Goals, and Moderate Multiple-Goals—that were highly similar across math and literacy (contingency coefficient = 0.59). Schoolchildren endorsing the Approach-Oriented profile demonstrated higher achievement compared to those with High Multiple-Goals or Moderate Multiple-Goals profiles, which involved more avoidance goals and were less adaptive (with up to 8% of variance explained by profile). Gender differences were observed: girls were more likely to endorse profiles combining multiple goals, whereas boys more often endorsed mastery or approach profiles. These results highlight early inter-individual differences in motivational development, observable in both mathematics and literacy. Promoting adaptive goal profiles in early education may enhance academic engagement and help reduce emerging motivational disparities. Full article

Background: Pulmonary arterial pressure is a key parameter for diagnosing cardiovascular and pulmonary diseases. Its measurement through right heart catheterization is considered the gold standard, and it is an invasive procedure that entails significant risks for patients. This has motivated the development of non-invasive techniques based on patient-specific imaging, such as Physics-Informed Neural Networks (PINNs), which integrate clinical measurements with physical models, such as the 1D reduced Navier–Stokes model, enabling biologically plausible predictions with limited data. Methods: This work implements a PINN model that uses velocity and area measurements in the main bifurcation of the pulmonary artery, comprising the main artery and its secondary branches, to predict pressure, velocity, and area variations throughout the bifurcation. The model training includes penalties to satisfy the laws of flow and momentum conservation. Results: The results show that, using 4D Flow MRI images from a healthy patient as clinical data, the pressure estimates provided by the model are consistent with the expected ranges reported in the literature, reaching a mean arterial pressure of 21.5 mmHg. Conclusions: This model presents an innovative approach that avoids invasive methods, being the first study to apply PINNs to estimate pulmonary arterial pressure in bifurcations. In future work, we aim to validate the model in larger populations and confirm pulmonary hypertension cases diagnosed through catheterization. Full article

This study introduces a novel deep learning architecture for vessel trajectory prediction based on Automatic Identification System (AIS) data. The motivation stems from the increasing importance of maritime transport and the need for intelligent solutions to enhance safety and efficiency in congested waterways—particularly with respect to collision avoidance and real-time traffic management. Special emphasis is placed on river navigation scenarios that limit maneuverability with the demand of higher forecasting precision than open-sea navigation. To address these challenges, we propose a Principal Component Analysis (PCA)-driven layered attention mechanism integrated within an encoder–decoder model to reduce redundancy and enhance the representation of spatiotemporal features, allowing the layered attention modules to focus more effectively on salient positional and movement patterns across multiple time steps. This dual-level integration offers a deeper contextual understanding of vessel dynamics. A carefully designed evaluation framework with statistical hypothesis testing demonstrates the superiority of the proposed approach. The model achieved a mean positional error of 0.0171 nautical miles (SD: 0.0035), with a minimum error of 0.0006 nautical miles, outperforming existing benchmarks. These results confirm that our PCA-enhanced attention mechanism significantly reduces prediction errors, offering a promising pathway toward safer and smarter maritime navigation, particularly in traffic-critical riverine systems. While the current evaluation focuses on short-term horizons in a single river section, the methodology can be extended to complex environments such as congested ports or multi-ship interactions and to medium-term or long-term forecasting to further enhance operational applicability and generalizability. Full article